This course immerses participants in the realm of Unsupervised Machine Learning, focusing on the principles, algorithms, and practical applications. Students will explore how unsupervised learning methods reveal latent patterns, relationships, and structures within data without relying on labeled training examples.
Key Content:
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Introduction to Unsupervised Learning:
- Differentiating Unsupervised and Supervised Learning
- Categories: Clustering and Dimensionality Reduction
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Clustering Algorithms:
- K-Means, Hierarchical, DBSCAN, GMM
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Dimensionality Reduction Techniques:
- PCA, t-SNE, Autoencoders
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Association Rule Mining:
- Apriori, FP-Growth, Market Basket Analysis
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Anomaly Detection:
- Isolation Forest, One-Class SVM
- Applications in Fraud Detection and Cybersecurity
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Real-world Applications:
- Case Studies in Healthcare, Finance, Marketing
- Ethical Considerations in Unsupervised Learning
Course Format:
- Theoretical lectures providing a foundational understanding.
- Hands-on practical sessions using popular ML libraries (e.g., scikit-learn, TensorFlow).
- Group projects encouraging application of unsupervised learning to real-world datasets.
- Guest lectures from industry experts sharing insights and ethical considerations.
This course empowers participants with skills to harness unsupervised learning for data exploration, pattern recognition, and decision-making in diverse fields, making it suitable for data scientists, analysts, and aspiring machine learning enthusiasts.
